Easy and Rapid Differentiation of Embryonic Stem Cells into Functional Motoneurons Using Sonic Hedgehog‐Producing Cells
Bibliographic record
Abstract
Directing embryonic stem (ES) cells to differentiate into functional motoneurons has proven to be a strong technique for studying neuronal development as well as being a potential source of tissue for cell replacement therapies involving spinal cord disorders. Unfortunately, one of the mitogenic factors (i.e., sonic hedgehog agonist) used for directed differentiation is not readily available, and thus this technique has not been widely accessible. Here, we present a novel and simple method to derive motoneurons from ES cells using readily attainable reagents. ES cells were derived from a mouse in which enhanced green fluorescent protein (eGFP) was linked to a motoneuron specific promoter. The cells were plated onto a monolayer of 293 EcR-Shh cells that carry an integrated construct for the expression of sonic hedgehog (Shh) under ecdysone-inducible control. To initiate motoneuron differentiation, 293 EcR-Shh:ES cell cocultures were treated with ponasterone A (PA) and retinoic acid for 5 days. PA induces ecdysone, and thus drives Shh expression. To assess differentiation, putative ES cell-derived motoneurons were studied immunocytochemically and cultured on chick myotubes for functional analysis. We found that ES cells differentiated into eGFP+ cells that expressed transcription factors typical of motoneurons. Furthermore, ES cell-derived motoneurons were capable of forming functional connections with muscle fibers in vitro. Finally, when transplanted into the developing chick spinal cord, ES cell-derived motoneurons migrated to the ventral horn and projected axons to appropriate muscle targets. In summary, this simple treatment paradigm produces functional motoneurons that can be used for both developmental and preclinical studies. Disclosure of potential conflicts of interest is found at the end of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".